JEL模型高效准确地将新闻提及链接到知识图谱实体，提升金融领域信息处理效率。
JEL: A Novel Model Linking Knowledge Graph entities to News Mentions
- 基于多神经网络的端到端实体链接方法，计算效率高。
- 性能超越现有最优模型，精准匹配新闻提及与知识图谱实体。
- 适合金融行业新闻分析、知识图谱应用等场景使用。
我们提出JEL，一种新型的计算高效、端到端的多神经网络实体链接模型，其性能优于当前最优模型。知识图谱已成为捕捉关键实体间关系并整合异构数据源的有力抽象。利用知识图谱的核心挑战在于，将文本来源（如新闻、博客）中的提及（如人名、公司名）正确链接到知识图谱中的实体，因为每个提及可能对应数千个候选实体。这一任务称为实体链接（EL），是自然语言处理的基础任务，在构建新闻分析平台等场景中具有重要意义。在摩根大通，新闻分析是一项关键任务，据创新数字团队调查，公司内约有25个团队正在积极寻求新闻分析解决方案，每年外部供应商支出超过200万美元。实体链接对于连接非结构化新闻文本与知识图谱至关重要，使用户能够访问知识图谱中大量经过整理的数据，显著提升日常工作效能。
原文摘要 · Abstract (English)
We present JEL, a novel computationally efficient end-to-end multi-neural network based entity linking model, which beats current state-of-art model. Knowledge Graphs have emerged as a compelling abstraction for capturing critical relationships among the entities of interest and integrating data from multiple heterogeneous sources. A core problem in leveraging a knowledge graph is linking its entities to the mentions (e.g., people, company names) that are encountered in textual sources (e.g., news, blogs., etc) correctly, since there are thousands of entities to consider for each mention. This task of linking mentions and entities is referred as Entity Linking (EL). It is a fundamental task in natural language processing and is beneficial in various uses cases, such as building a New Analytics platform. News Analytics, in JPMorgan, is an essential task that benefits multiple groups across the firm. According to a survey conducted by the Innovation Digital team 1 , around 25 teams across the firm are actively looking for news analytics solutions, and more than \$2 million is being spent annually on external vendor costs. Entity linking is critical for bridging unstructured news text with knowledge graphs, enabling users access to vast amounts of curated data in a knowledge graph and dramatically facilitating their daily work.
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